Input validation and sanitization in Agentic AI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why is input validation important in AI systems?

Imagine you have an AI model that predicts loan approvals. Why should you validate the input data before feeding it to the model?

A To increase the size of the dataset by adding random values.

B To make the model run faster by skipping some data points.

C To ensure the data is clean and within expected ranges, preventing wrong predictions.

D To avoid training the model on too much data.

💡 Hint

Think about what happens if the model gets unexpected or wrong data.

Input validation helps catch errors or unexpected values early. This prevents the AI from making wrong predictions or crashing.

❓ Predict Output

intermediate

Output of input sanitization function

What is the output of this Python function that sanitizes input text by removing digits?

Agentic AI

def sanitize(text):
    return ''.join(c for c in text if not c.isdigit())

result = sanitize('Agent007 is ready!')
print(result)

A Agent is ready!

B Agent007 is ready!

C 007 is ready!

D Agent is ready!

💡 Hint

Look at how digits are filtered out from the string.

The function removes all characters that are digits. So '007' is removed from 'Agent007'.

❓ Model Choice

advanced

Choosing a model robust to noisy input data

You have input data that may contain some errors or noise. Which model type is best to handle this situation?

A A simple linear regression model without regularization

B A deep neural network with dropout and batch normalization

C A decision tree with no pruning

D A nearest neighbor model using raw input features

💡 Hint

Think about models that include techniques to reduce overfitting and handle noise.

Deep neural networks with dropout and batch normalization help the model generalize better and reduce sensitivity to noisy inputs.

❓ Metrics

advanced

Evaluating input sanitization impact on model accuracy

You trained two models: one on raw input data and one on sanitized input data. The sanitized model has 5% higher accuracy. What does this suggest?

A Sanitizing input data improved data quality, helping the model learn better.

B Accuracy is not affected by input data quality.

C The model with raw data is always better regardless of accuracy.

D Sanitizing input data removed useful information, hurting the model.

💡 Hint

Think about how cleaning data affects learning.

Removing noise or invalid data through sanitization helps the model focus on meaningful patterns, improving accuracy.

🔧 Debug

expert

Identifying input validation bug in AI pipeline

Consider this code snippet that validates numeric input features before prediction. What error will it raise?

Agentic AI

def validate_features(features):
    for key, value in features.items():
        if value < 0:
            raise ValueError(f"Negative value for {key}")

input_data = {'age': 25, 'income': -5000, 'score': 70}
validate_features(input_data)

A TypeError: unsupported operand type(s) for <: 'str' and 'int'

B No error, function runs successfully

C KeyError: 'income'

D ValueError: Negative value for income

💡 Hint

Check which input value is negative and what the function does.

The function raises a ValueError when it finds a negative value. 'income' is -5000, so the error message mentions 'income'.

Practice

1. What is the main purpose of input validation in machine learning systems?

easy

A. To train the model with new data

B. To clean the data by removing unwanted characters

C. To check if the input data is the correct type and format

D. To store data securely in a database

Solution

1. Step 1: Understand input validation

   Input validation means checking if the data is the right type and format before using it.

2. Step 2: Differentiate from sanitization

   Input sanitization cleans data, but validation focuses on correctness and format.

3. Final Answer:

   To check if the input data is the correct type and format -> Option C

4. Quick Check:

   Input validation = Check data type and format [OK]

Hint: Validation means checking data type and format [OK]

Common Mistakes:

• Confusing validation with sanitization
• Thinking validation trains the model
• Assuming validation stores data

2. Which of the following is the correct way to validate that an input is a positive integer in Python?

easy

A. if isinstance(input_value, int) and input_value > 0:

B. if type(input_value) == 'int' and input_value > 0:

C. if input_value.isdigit() and input_value > 0:

D. if input_value > 0:

Solution

1. Step 1: Check type correctly

   Use isinstance(input_value, int) to check if input is an integer.

2. Step 2: Check positivity

   Ensure the integer is greater than zero with input_value > 0.

3. Final Answer:

   if isinstance(input_value, int) and input_value > 0: -> Option A

4. Quick Check:

   Use isinstance and > 0 for positive integer check [OK]

Hint: Use isinstance() to check type, then compare value [OK]

Common Mistakes:

• Using type() == 'int' (wrong syntax)
• Calling isdigit() on non-string input
• Skipping type check before comparison

3. Given the code below, what will be the output?

def sanitize_input(text):
    return text.strip().lower()

user_input = '  Hello World!  '
cleaned = sanitize_input(user_input)
print(cleaned)

medium

A. Hello World!

B. !dlroW olleH

C. HELLO WORLD!

D. hello world!

Solution

1. Step 1: Understand strip()

   The strip() method removes spaces from the start and end of the string.

2. Step 2: Understand lower()

   The lower() method converts all letters to lowercase.

3. Final Answer:

   hello world! -> Option D

4. Quick Check:

   strip + lower = 'hello world!' [OK]

Hint: strip removes spaces, lower makes all letters small [OK]

Common Mistakes:

• Ignoring strip() effect on spaces
• Confusing lower() with upper()
• Expecting original casing in output

4. Identify the error in this input validation code snippet:

def validate_age(age):
    if age.isdigit() and age > 0:
        return True
    else:
        return False

medium

A. Comparing string with integer using > operator

B. Using isdigit() on a non-string type

C. Missing return statement in else block

D. Function name is invalid

Solution

1. Step 1: Check isdigit() usage

   isdigit() works on strings, so age should be a string here.

2. Step 2: Identify type mismatch in comparison

   Comparing age > 0 compares string to int, which causes error.

3. Final Answer:

   Comparing string with integer using > operator -> Option A

4. Quick Check:

   String > int comparison causes error [OK]

Hint: Check types before comparing values [OK]

Common Mistakes:

• Assuming isdigit() converts type
• Ignoring type mismatch in comparisons
• Thinking function name affects validation

5. You receive user data as a list of strings representing ages: ['25', ' 30', 'twenty', '40', '']. Which code snippet correctly validates and sanitizes this data to keep only valid positive integers?

hard

A. valid_ages = [age for age in ages if age.isdigit() and age > 0]

B. valid_ages = [int(age.strip()) for age in ages if age.strip().isdigit() and int(age.strip()) > 0]

C. valid_ages = [int(age) for age in ages if age.isnumeric()]

D. valid_ages = [int(age) for age in ages if age.strip() != '']

Solution

1. Step 1: Sanitize input by stripping spaces

   Use age.strip() to remove spaces before validation.

2. Step 2: Validate with isdigit() and positive check

   Check if stripped string is digits only and convert to int to check > 0.

3. Step 3: Convert valid strings to integers

   Use int(age.strip()) to convert valid strings to integers.

4. Final Answer:

   valid_ages = [int(age.strip()) for age in ages if age.strip().isdigit() and int(age.strip()) > 0] -> Option B

5. Quick Check:

   Strip spaces, check digits, convert to int > 0 [OK]

Hint: Strip spaces before isdigit(), then convert and check > 0 [OK]

Common Mistakes:

• Not stripping spaces before validation
• Comparing strings directly to numbers
• Including empty or non-digit strings